Waste analysis system and method

ABSTRACT

Embodiments of the present invention provide techniques for identifying and quantifying waste in a process. Waste information is input via images and/or natural language. The amount of waste is estimated based on information in images and/or a natural language description. A computer-implemented technique extracts metadata on waste products from the images and/or natural language description. A variety of factors such as social media trends, weather, traffic, and/or sports schedules are evaluated by the computer and used in predicting the amount of waste that will occur.

RELATED APPLICATIONS

The present patent document is a continuation of U.S. patent applicationSer. No. 15/433,355 filed Feb. 15, 2017, the entire contents of which isincorporated herein by reference.

FIELD OF THE INVENTION

Embodiments of the invention relate to waste analysis systems andmethods.

BACKGROUND

Food service businesses, such as restaurants, cafés, cafeterias, etc.waste a large amount of food every year. In addition to this beingunfortunate since hunger is still a big problem in the U.S. and aroundthe world, it is also bad for the food service business's bottom line.When more food is purchased than needed, the food may spoil, and themoney spent on that food is wasted. With many food service operationsrunning on very small margins, improved methods and systems ofpredicting and preventing waste are needed.

SUMMARY

In one aspect, there is provided a computer-implemented method foridentifying waste in a process, comprising: acquiring metadata for adiscarded product in a waste product analysis system computer comprisinga processor, wherein the processor performs functions of: recording themetadata; analyzing the metadata with a rules engine to derive asuggestion for waste reduction; and generating a report based on therecorded metadata, wherein the report includes the suggestion for wastereduction.

In another aspect, there is provided a computer system comprising: aprocessor; a memory coupled to the processor, the memory containinginstructions, that when executed by the processor, perform the steps of:acquiring metadata for a discarded product; recording the metadata;analyzing the metadata with a rules engine to derive a suggestion forwaste reduction; and generating a report based on the recorded metadata,wherein the report includes the suggestion for waste reduction.

In yet another aspect, there is provided a computer program product foridentifying waste in a process, for an electronic computing devicecomprising a computer readable storage medium having programinstructions embodied therewith, the program instructions executable bya processor to cause the electronic device to: acquire metadata for adiscarded product; record the metadata; analyze the metadata with arules engine to derive a suggestion for waste reduction; and generate areport based on the recorded metadata, wherein the report includes thesuggestion for waste reduction.

BRIEF DESCRIPTION OF THE DRAWINGS

Features of the disclosed embodiments will be more readily understoodfrom the following detailed description of the various aspects of theinvention taken in conjunction with the accompanying drawings.

FIG. 1 is a block diagram of a device in accordance with embodiments ofthe present invention.

FIG. 2 shows an environment for implementation of embodiments of thepresent invention.

FIG. 3 shows an example of an image-based embodiment of the presentinvention.

FIG. 4 shows an example image for an image-based embodiment of thepresent invention.

FIG. 5 shows an example report generated in accordance with embodimentsof the present invention.

FIG. 6 shows an example of disambiguation in accordance with embodimentsof the present invention.

FIG. 7 shows an example of a natural language processing embodiment ofthe present invention.

FIG. 8 is a flowchart indicating process steps for embodiments of thepresent invention.

The drawings are not necessarily to scale. The drawings are merelyrepresentations, not necessarily intended to portray specific parametersof the invention. The drawings are intended to depict only exampleembodiments of the invention, and therefore should not be considered aslimiting in scope. In the drawings, like numbering may represent likeelements. Furthermore, certain elements in some of the figures may beomitted, or illustrated not-to-scale, for illustrative clarity.

DETAILED DESCRIPTION

Embodiments of the present invention provide techniques for identifyingand quantifying waste in a process. Waste information is input viaimages and/or natural language. The amount of waste is estimated basedon information in images and/or a natural language description. Avariety of non-limiting factors such as social media trends, weather,traffic, and/or sports schedules are used in predicting the amount ofwaste that will occur.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of this disclosure.As used herein, the singular forms “a”, “an”, and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. Furthermore, the use of the terms “a”, “an”, etc., do notdenote a limitation of quantity, but rather denote the presence of atleast one of the referenced items. It will be further understood thatthe terms “comprises” and/or “comprising”, or “includes” and/or“including”, when used in this specification, specify the presence ofstated features, regions, integers, steps, operations, elements, and/orcomponents, but do not preclude the presence or addition of one or moreother features, regions, integers, steps, operations, elements,components, and/or groups thereof.

Reference throughout this specification to “one embodiment,” “anembodiment,” “some embodiments”, or similar language means that aparticular feature, structure, or characteristic described in connectionwith the embodiment is included in at least one embodiment of thepresent invention. Thus, appearances of the phrases “in one embodiment,”“in an embodiment,” “in some embodiments”, and similar languagethroughout this specification may, but do not necessarily, all refer tothe same embodiment.

Moreover, the described features, structures, or characteristics of theinvention may be combined in any suitable manner in one or moreembodiments. It will be apparent to those skilled in the art thatvarious modifications and variations can be made to the presentinvention without departing from the spirit and scope and purpose of theinvention. Thus, it is intended that the present invention cover themodifications and variations of this invention provided they come withinthe scope of the appended claims and their equivalents. Reference willnow be made in detail to the preferred embodiments of the invention.

FIG. 1 is a block diagram of a device 100 in accordance with embodimentsof the present invention. Device 100 is shown as a simplified diagram ofmodules. Device 100 is an electronic computing device. Device 100includes a processor 102, which is coupled to a memory 104. Memory 104may include dynamic random access memory (DRAM), static random accessmemory (SRAM), magnetic storage, and/or a read only memory such asflash, EEPROM, optical storage, or other suitable memory. In someembodiments, the memory 104 may not be a transitory signal per se.Memory 104 includes instructions, which when executed by the processor,implement steps of the present invention. In embodiments, device 100 mayhave multiple processors 102, and/or multiple cores per processor.

Device 100 may further include storage 106. In embodiments, storage 106may include one or more magnetic storage devices such as hard diskdrives (HDDs). Storage 106 may include one or more solid state drives(SSDs). Any other storage device may be included instead of, or inaddition to, those disclosed herein.

Device 100 further includes a user interface 108, examples of whichinclude a liquid crystal display (LCD), a plasma display, a cathode raytube (CRT) display, a light emitting diode (LED) display, an organic LED(OLED) display, or other suitable display technology. The user interface108 may include a keyboard, mouse, and/or a touch screen, incorporatinga capacitive or resistive touch screen in some embodiments.

The device 100 further includes a communication interface 110. In someembodiments, the communication interface 110 may include a wirelesscommunication interface that includes modulators, demodulators, andantennas for a variety of wireless protocols including, but not limitedto, Bluetooth™, Wi-Fi, and/or cellular communication protocols forcommunication over a computer network. Any communication interface, nowknown or hereafter developed, may be substituted.

The device 100 further includes a geolocation receiver 112. Receiver 112receives geolocation data, from sources such as Global PositioningSystem (GPS), GLONASS, or other suitable geolocation systems.

FIG. 2 shows an environment 200 for implementation of embodiments of thepresent invention. Waste product analysis system 202 includes aprocessor 240, memory 242, and storage 244. A set of instructions 247 isstored in memory 242 for execution by the processor 240 to implementembodiments of the invention. System 202 is in communication withnetwork 224. Network 224 may be the Internet, a wide area network, alocal area network, a virtual private network, a cloud network, or anyother suitable network.

Waste product analysis system 202 acquires metadata for a discardedproduct and records the metadata. Waste product analysis system 202 mayextract the metadata from information transmitted from client devices ofusers. Client devices 216 and 218 are devices that users use to, amongother things, input data for transmission over the network 224 to system202. Client devices 216 and 218 may be mobile phones, smartphones,tablet computers, desktop computers, laptop computers, or any othersuitable device. It should be recognized that although only two clientdevices are shown, more or fewer may be included within the scope andspirit of the invention. Information about waste may be input to aclient device using an onboard, attached, or wirelessly coupledmicrophone, physical keyboard or touch screen, camera, or other inputmechanism. The data may be captured through speech analysis(speech-to-text), image analysis, or other suitable mechanism.

Computer vision system 230 is also in communication with network 224.Computer vision system 230 is in communication with camera 232 or otherdevice for capturing data about waste (for example, a barcode scanner,RFID tag scanner, etc.). Camera 232 takes images of waste. The waste maybe on a conveyor belt, as discussed further with regard to FIG. 3.Computer vision system 230 may process images and send one or more ofsuch images to waste product analysis system 202 for further processing.

Waste product analysis system 202 analyzes the information received fromclient devices and/or computer vision system 230 to derive metadataabout wasted products. System 202 also generates a report based on therecorded metadata related to factors such as social media trends,weather forecasts, traffic reports, and sports schedules. The report mayinclude a set of data. The set of data may include (at least one)suggestions for waste reduction. Another example of included data is anindicator of potential waste (i.e. an indication that due to one or morefactors, there is a potential for waste).

Embodiments may include using social media data to derive the indicatorof potential waste. Social media server 204 may store social media datafrom social networking websites, like Facebook®, Twitter®, etc. System202 may analyze data received from social media server 204 to determineif any trends may be detected. For example, system 202 may use languageanalysis to analyze keyword data to determine that 100 people within 25miles of a restaurant have used the terms “going on vacation” or“leaving town” in social media posts within the past two days.Accordingly, the suggestion in the report to the restaurant may be toorder less food for the coming week since, unlike usual, many peoplewill not be around town.

Embodiments may include using weather data to derive the indicator ofpotential waste. Weather forecast server 206 may store weather data,such as weather reports and predictions received from media outlets, orgovernmental and quasi-governmental weather prediction services, such asthe National Weather Service, etc. System 202 may analyze weather datareceived from the weather forecast server, and determine based thereon,that a storm is coming. Based on evaluation of historical data, thesystem 202 may correlate that when big storms hit in the particulargeographical area at issue, there is a surge in people ordering take-outfood. Accordingly, the suggestion may include ordering more ingredientsin advance of the storm.

Embodiments may include using traffic data to derive the indicator ofpotential waste. Traffic server 212 may store data relating to trafficpatterns received from media reports, government agency reports,crowdsourced traffic data, etc. System 202 may analyze the data receivedfrom server 212, and based on the analysis determine that due toconstruction near a restaurant, there will be traffic delays in thevicinity for a week. Based on an evaluation of historical data, system202 may determine that nearby traffic jams correlate with fewercustomers visiting the restaurant. Accordingly, system 202 makes asuggestion to decrease the number of pizzas to be prepared compared to atypical/average week, and therefore an appropriate amount of suppliesare purchased for that week.

Embodiments may include using sports schedule data to derive theindicator of potential waste. Sports schedule server 208 may store datarelating to sports games. The information may be stored in, e.g., anelectronic calendar and/or database. System 202 may analyze the datareceived from server 208. Based on the analysis, system 202 maydetermine that there are three college football games occurring in thecoming week. Based on an evaluation of historical data, system 202 maydetermine that a college football game correlates with more customerscalling for delivery of food. Accordingly, system 202 makes a suggestionto increase the number of pizzas prepared, and therefore appropriatesupplies purchased, for that week.

It should be recognized that the above-identified sets of data,analysis, evaluations, etc., are examples, and that in implementationsof embodiments, more, fewer, or different steps, servers, data, andreporting may be included without departing from the scope of theinvention.

Point of Sale (PoS) system 214 represents a computer terminal wherecustomer orders, such as food orders, are entered. The PoS 214 recordsthe types of items sold and the amount of each item sold. Thisinformation can be provided to the waste product analysis system 202.The system 202 can obtain historical consumption data based on recordsfrom the PoS system 214. Weekly, monthly, and yearly trends can beanalyzed to predict when a condition for potential waste can occur. Forexample, if the system 202 identifies a trend that during the first weekof each month consumption of a product is lower than the other weeks ofthe month, then the report can suggest purchasing less perishable foodinventory for the first week of the month.

FIG. 3 shows an example of an image-based embodiment 300 of the presentinvention. As shown, in the example, there is a conveyor belt 314 wherecustomers place their trays when finished in a cafeteria. The trays,examples shown as 320, 322, and 324, having the trash/leftovers on topare moved by the conveyor belt 314 in direction D toward the trashcenter on the conveyor belt. Waste product analysis system 310 issimilar to the waste product analysis system 202 of FIG. 2. Itcommunicates over network 308 (similar to network 224 of FIG. 2) withcomputer vision system 306.

Computer vision system 306 is coupled with camera 304. An image of thediscarded product is received. In the example embodiment, computervision system 306 receives a set of (at least one) images from camera304. An object recognition process is performed on the received image toidentify one or more discarded products within the image. The objectrecognition process may perform a variety of image processing techniquessuch as edge detection, shape detection, clustering, and/orprobabilistic classifiers to determine the discarded objects. Imagemetadata is extracted from the received image based on the objectrecognition process.

The object recognition analysis may include performing an edge detectionprocess to determine the outer edges of the objects on the tray (i.e.the items for discarding). The computer vision system may include adatabase having the shapes of various foods stored therein. For example,the shape of a pear may be stored in association with “pear”. The shapeof an apple may be stored in association with “apple”. For edgedetection, some embodiments may utilize a Canny edge detector algorithm.Some embodiments may utilize mathematical models, such as a deformablecontour model, and/or an active shape model for detection of thediscarded objects.

The object recognition analysis may instead, or in addition to, performa color analysis. Computer vision system 306 may include a database thatincludes information about food objects in association with theircolors. For example, it may associate the color green with legumes. Itmay associate red with ketchup and tomatoes.

In embodiments, metadata is acquired for a discarded product. In someembodiments, the discarded product is a discarded food product. Inembodiments, performing an object recognition process further comprisesusing one or more image classifiers. The image classifiers can betrained using a supervised learning technique.

In some embodiments, an object count process is performed on thereceived image. Sales data is received for a predetermined time durationfrom a point of sale system, and an amount of used product during thepredetermined time duration is determined based on the received data.The rules engine determines an overage amount of product as a functionof the acquired metadata for the discarded product and a factor of theamount of used product. Accordingly, a recommendation can then be madebased on how much product was sold, and how much was discarded. Forexample, if the restaurant normally sells 100 tomatoes per day, and itis determined that on average 30 per day are discarded, the system 202can recommend reducing the number of tomatoes to be purchased in futureorders. A margin of error may be established (for example, 10 percent).System 202 may accordingly calculate that 100 tomatoes were sold, and10% of that is 10, so since 30 tomatoes per day are being discarded, therecommendation would be to cut back to purchase 20 less tomatoes perday. This leaves a 10 percent margin to cover a positive fluctuation insales.

FIG. 4 shows an example image 400 for an image-based embodiment of thepresent invention. In the example, the image is taken from above a foodtray 324. The food tray has two compartments 332 and 334. Lettuce leaf336 is in the first compartment 332, and some pickle slices 340, 342,344, and 346 are left over in compartment 334. In embodiments, acquiringmetadata for a discarded product comprises acquiring metadata for a foodproduct. Metadata, and/or attributes like shape and color of a foodproduct like an apple may be acquired initially and stored in adatabase, so that a discarded apple can be recognized by the computervision system when it is discarded. Embodiments may include performingan object count process on the received image. In the example, theembodiment will count one piece of lettuce and four pickle slices. Basedon the computer-implemented analysis, a cafeteria may make menu changesto reduce waste. Based on the previous example, the cafeteria maysubstitute a piece of wax paper for the lettuce, and only include twopickle slices instead of four, as an example.

In some embodiments, when objects in the image cannot be recognized witha high confidence level, the user may be prompted to enter additionalinformation regarding the waste that is shown in one or more images. Forexample, based on what is shown in FIG. 4, if the system cannot identifythe objects 340, 342, 344, and 346 as pickles, it may prompt the user toidentify those objects in the image. Over time, using image classifiers,neural networks, or other suitable machine learning techniques, thesystem may learn to recognize various waste items.

FIG. 5 shows an example report 500 generated in accordance withembodiments of the present invention. Generating the report may includeseveral steps, and the report may include several elements. The reportincludes the location to which it corresponds, at 510. The location maybe extracted from memory of a previously stored manually entered userinput, or determined from a geolocation receiver using, e.g., a globalpositioning satellite system, such as GPS. In the embodiments wherephotographs of waste are uploaded from a mobile device for furtheranalysis, location data, such as longitude and latitude in an imageheader, may be used to determine a location. In cases where a businesshas multiple locations, using location information allows an analysis ofwaste in each location. The waste output of each location can becompared, and locations outputting the most waste can be furtherexamined to determine if there are any ways to address the higher levelsof waste output.

In some embodiments, generating the report may include calculatingaverage daily waste, shown at 512. In the example, the calculationindicates that an average of one leaf of lettuce and four pickle slicesare discarded with each meal.

In some embodiments, generating the report may include calculating anaverage wholesale cost of waste per meal, shown at 514. For example, theamount of waste per day over an amount of time (for example, one week or30 days), is totaled. That number is then divided by the number of days.The result is then divided by the number of meals per day served by theestablishment.

In some embodiments, generating the report may include determining anoverage amount of used product during a predetermined time duration,shown at 518. The overage amount provides an indication of the amount ofwaste (discarded product). In some embodiments, analyzing the metadatawith a rules engine comprises receiving sales data for a predeterminedtime duration from a point of sale system and determining an amount ofused product during the predetermined time duration based on thereceived data. The rules engine determines an overage amount of productas a function of the acquired metadata for the discarded product and afactor of the amount of used product. In the example, it is 1,020pickles and 740 lettuce leaves that are determined to be the overageamount for the predetermined week. The determination of the overageamount may include a margin based on sales reported from a Point of Sale(Pos) system.

In some embodiments, generating the report may further includegenerating an indicator of potential waste, shown at 522. Embodimentsmay include using an electronic calendar from a sports schedule serverto extract data. In the example, an electronic calendar of a sports teamschedule is used to derive the indicator of potential waste. Forexample, if there is an away professional baseball game, and therestaurant is located instead near the home stadium, the away game mayindicate a likelihood of potential waste since people will not benearby.

In some embodiments, generating the report may further includecalculating average daily waste, shown at 520. This calculation can bemade by aggregating the determined daily waste over a period of time.For example, it may be calculated over the period of a week. In thecase, the total amount of waste from the week can be divided by sevendays, for a total of the average daily waste.

In some embodiments, generating the report may further include renderinga trend graph. In the example, a chart is shown having waste amount indollars on the y-axis at 504 and the days of the week on the x-axis at502. Bar indicators show the amount of waste in dollars per day.

It should be recognized that in some embodiments, the generating of thereport may include more or fewer steps where feasible. It should also berecognized that the report may include more or fewer elements displayedthereon.

FIG. 6 shows an example 600 of disambiguation in accordance withembodiments of the present invention. Disambiguation is one of theprocesses that may be utilized in embodiments of the present invention.As part of content ingest, text may be tokenized into words and taggedwith parts of speech. For some words, there can be more than one meaningand/or part of speech. FIG. 6 shows a disambiguation example with theword “saw.” In phrase 601, the word “saw” 602 is used as a past tenseverb. In embodiments, a machine learning natural language analysismodule may identify the prior token 604 to the word “saw” as a pronoun,and the following token 603 as an article. In training a classifier, thepattern of pronoun-token-article may be associated with a verb, and thusthe token is interpreted as a verb.

In phrase 605, the word “saw” 606 is a noun for a cutting tool. Inembodiments, a machine learning natural language analysis module mayidentify the prior token 608 to the word saw as an article, and thefollowing token 609 as a verb. In training a classifier, the patternarticle-token-verb may be associated with a noun, and thus the token isinterpreted as a noun.

In phrase 611, the word “saw” 610 is a noun for a cutting tool. Inembodiments, a machine learning natural language analysis module mayidentify the prior token 612 to the word “saw” as part of an infinitiveform, and the following token 615 as an article. In training aclassifier, the pattern “to”-token-article may be associated with averb, and thus the token is interpreted as a verb. These classifiers andtechniques for disambiguation are merely examples, and other classifiersand techniques are possible.

FIG. 7 shows an example 700 of a natural language processing embodimentof the present invention. In embodiments, acquiring metadata for adiscarded product comprises natural language processing. A naturallanguage description of a discarded product is received. In someembodiments, the natural language description may be received in a formof user input of text. In some embodiments, the natural languagedescription may comprise a speech fragment. A speech-to-text process isperformed on the speech fragment prior to performing the naturallanguage analysis.

A natural language analysis of the natural language description isperformed to identify one or more discarded products within the naturallanguage description. An entity detection process is performed for thenatural language description. The entity detection can include nounidentification, followed by identifying a subset of nouns includingproper nouns, and nouns deemed to be topically pertinent. The entitydetection can include identification of entity relationships. Entityrelationships can include, but are not limited to, “is a kind of,”“entails,” “pertains to,” “is a member of,” “is a part of,” “is aninstance of,” “causes,” “is an opposite of,” and others. In someembodiments, entities can have more than one entity relationship betweenthem. Other entity relationships are possible.

In embodiments, performing a natural language analysis includesperforming at least one of bigram processing, disambiguation,part-of-speech analysis, and anaphora resolution.

The example 700 further illustrates the use of a bigram analysis inaccordance with embodiments of the present invention. In a bigramanalysis, a pair of words in a particular order may be searched within abody of text of an input query. In this example, a user 707 utters aphrase 717 that is converted from text to speech to form a text excerpt.The bigram “egg noodles” is located within the text excerpt. Twooccurrences, indicated as 702A and 702B, are present in the textpassage. In embodiments, the usage of bigrams, trigrams, or moregenerally, n-grams, may be used to improve relevance in processing anatural language input query. Thus, embodiments include performing acomputerized natural language analysis process to derive sentenceclassifications on the input query by performing a bigram analysis.

The natural language analysis process can include, but is not limitedto, indexing, concordance, stop word processing, bigram processing,dispersion analysis, lexical richness analysis (ratio of distinct wordsto total words), disambiguation, part-of-speech analysis, and/oranaphora resolution (the process of identifying what a pronoun or nounphrase refers to). Additionally, the natural language analysis processcan include the use of trained classifiers, including, but not limitedto, decision trees, naive Bayes classifiers, Maximum Entropyclassifiers, decision trees, and/or support vector machines.

In embodiments, generating the report further includes generating asubstitution suggestion, as shown at 712. In the example, adetermination has been made that fresh fish is being discarded at a rateabove a predetermined threshold. The waste product analysis system mayinclude a database of substitutable products with a longer shelf life.The database can include the information that frozen fish has a longershelf life than fresh fish, and that frozen fish can be used as asubstitute for fresh fish. Accordingly, a suggestion is generated to usefrozen fish instead of fresh fish.

In embodiments, generating the report further includes generating aprocess change suggestion, as shown at 714. In the example, adetermination has been made that egg noodles were discarded. Asuggestion is generated to use desiccant when storing egg noodles in thefuture. The desiccant will keep the egg noodles fresh longer. Thesuggestion may be generated by a crawl of the Internet for informationabout storing egg noodles. The suggestion may originate from informationstored in a knowledge base within the waste product analysis system.Alternatively, entities, fragments, bigrams, etc., detected duringlanguage processing may be used as keywords for searching in the webcrawl. It should be recognized that web crawling is an example of howinformation may be located, and that any other suitable mechanism may besubstituted.

FIG. 8 is a flowchart 800 indicating process steps for embodiments ofthe present invention. Metadata is acquired for a product, at 850. Themetadata can include product type, product quantity, and/or productcost, among others. The acquiring of the metadata can come from imagesthat are acquired on a mobile device of a worker. Thus, a worker canphotograph discarded food products. If barcodes, QR codes, and/orproduct numbers are present on the packaging and/or directly on thediscarded food products, the waste product analysis system can use thosecodes to retrieve information about the product being discarded. In someembodiments, the metadata can be acquired through a natural languageprocess. Thus, a user may speak into their mobile device regarding whatis being discarded. Speech-to-text and natural language processing canbe used to extract the metadata from the spoken words of the worker.Metadata is recorded, at 852. The recording of metadata can be performedby storing the metadata in a database. In embodiments, the database usesa structured query language (SQL) format to store/retrieve data.Metadata is analyzed with a rules engine, at 854. The rules engine canestablish a variety of operating parameters, such as weighting factorsfor various conditions. For example, a sports team schedule may have aweighting factor of 2, while a blizzard forecast from a weather forecastserver may have a weighting factor of 5, indicating that the blizzardforecast has a greater impact on the potential for waste than a sportsteam schedule. Similar weighting factors can be used for social mediaand traffic. In embodiments, a waste potential score W is computed asfollows:

W=K1(S)+K2(F)+K3(M)+K4(T); where:

S is a sports schedule score;F is a weather forecast score;M is a social media score;T is a traffic score; andK1, K2, K3, and K4 are weighting factors.

The sports schedule score S is a numeric measure of a predicted impactof sports schedules on waste. As an example, when a local NFL footballteam has a bye week, the sports schedule score S may increase, sincethere can be less demand for food when the team is not playing on aparticular weekend.

The weather forecast score F is a numeric measure of a predicted impactof weather forecasts on waste. As an example, when a blizzard ortropical storm is predicted, the weather forecast score F may increase,since there can be less demand for food when the weather is poor sinceless people may choose to go out to a restaurant. The waste productanalysis system may import natural language weather forecasts, METARweather codes, or other suitable formats for weather forecasting andreporting.

The social media score M is a numeric measure of a predicted impact ofsocial media trends on waste. As an example, if a particular product istrending downward on social media (e.g. food with trans fats), thesocial media score may increase, since there can be less demand for foodof the type that is trending downward.

The traffic score T is a numeric measure of a predicted impact oftraffic on waste. As an example, if a particular road is blocked, basedon a traffic report, the traffic score T may increase, since there canbe less consumption of food if people cannot access a restaurant due totraffic.

A substitution is selected, at 856. The substitution can be based on adatabase of substitutable products. As an example, fresh carrots may bereplaced with frozen carrots, canned carrots, or some other form ofcarrots. In some cases, the substitution can be for a different productaltogether. For example, if the waste product analysis system detected alarge amount of red oak lettuce being discarded, the substitution ofspinach can be suggested, since spinach has a longer shelf life than redoak lettuce.

A report is generated, at 858. The report can include, but is notlimited to, a cost of waste per meal, a potential waste indicator, anaverage daily waste amount, a wholesale cost of waste per meal, and/or awaste trend chart. Other pieces of information may be included in someembodiments, including the location of the reported waste, the workerthat reported the waste, and the like.

As can now be appreciated, disclosed embodiments provide improvedcomputer-implemented techniques for identifying, predicting, andreducing waste in a process. While examples disclosed herein pertainedto food waste, other (non-food) processes may also benefit fromembodiments of the present invention. For example, a manufacturingprocess that uses epoxy can benefit from an automated waste analysis inaccordance with embodiments of the present invention. Various other usecases are possible. Natural language processing and/or image analysismake it simple and convenient for users to input wasted ingredients andquantities. They simply take photos of waste or describe the waste innatural language terminology. The computer-implemented techniquesdetermine the waste based on the user input and perform an analysis.Social media trends, point of sale data, weather, traffic, geolocationdata, and other external factors can be used to further enhance theanalysis. Thus, the disclosed embodiments can be used to identify,predict, and reduce waste in a variety of industries and applications.

Some of the functional components described in this specification havebeen labeled as systems or units in order to more particularly emphasizetheir implementation independence. For example, a system or unit may beimplemented as a hardware circuit comprising custom VLSI circuits orgate arrays, off-the-shelf semiconductors such as logic chips,transistors, or other discrete components. A system or unit may also beimplemented in programmable hardware devices such as field programmablegate arrays, programmable array logic, programmable logic devices, orthe like. A system or unit may also be implemented in software forexecution by various types of processors. A system or unit or componentof executable code may, for instance, comprise one or more physical orlogical blocks of computer instructions, which may, for instance, beorganized as an object, procedure, or function. Nevertheless, theexecutables of an identified system or unit need not be physicallylocated together, but may comprise disparate instructions stored indifferent locations which, when joined logically together, comprise thesystem or unit and achieve the stated purpose for the system or unit.

Further, a system or unit of executable code could be a singleinstruction, or many instructions, and may even be distributed overseveral different code segments, among different programs, and acrossseveral memory devices. Similarly, operational data may be identifiedand illustrated herein within modules, and may be embodied in anysuitable form and organized within any suitable type of data structure.The operational data may be collected as a single data set, or may bedistributed over different locations including over different storagedevices and disparate memory devices.

Furthermore, systems/units may also be implemented as a combination ofsoftware and one or more hardware devices. For instance, locationdetermination and alert message and/or coupon rendering may be embodiedin the combination of a software executable code stored on a memorymedium (e.g., memory storage device). In a further example, a system orunit may be the combination of a processor that operates on a set ofoperational data.

As noted above, some of the embodiments may be embodied in hardware. Thehardware may be referenced as a hardware element. In general, a hardwareelement may refer to any hardware structures arranged to perform certainoperations. In one embodiment, for example, the hardware elements mayinclude any analog or digital electrical or electronic elementsfabricated on a substrate. The fabrication may be performed usingsilicon-based integrated circuit (IC) techniques, such as complementarymetal oxide semiconductor (CMOS), bipolar, and bipolar CMOS (BiCMOS)techniques, for example. Examples of hardware elements may includeprocessors, microprocessors, circuits, circuit elements (e.g.,transistors, resistors, capacitors, inductors, and so forth), integratedcircuits, application specific integrated circuits (ASIC), programmablelogic devices (PLD), digital signal processors (DSP), field programmablegate array (FPGA), logic gates, registers, semiconductor devices, chips,microchips, chip sets, and so forth. However, the embodiments are notlimited in this context.

Also noted above, some embodiments may be embodied in software. Thesoftware may be referenced as a software element. In general, a softwareelement may refer to any software structures arranged to perform certainoperations. In one embodiment, for example, the software elements mayinclude program instructions and/or data adapted for execution by ahardware element, such as a processor. Program instructions may includean organized list of commands comprising words, values, or symbolsarranged in a predetermined syntax that, when executed, may cause aprocessor to perform a corresponding set of operations.

The present invention may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, may be non-transitory,and thus is not to be construed as being transitory signals per se, suchas radio waves or other freely propagating electromagnetic waves,electromagnetic waves propagating through a waveguide or othertransmission media (e.g., light pulses passing through a fiber-opticcable), or electrical signals transmitted through a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device. Program data may also bereceived via the network adapter or network interface.

Computer readable program instructions for carrying out operations ofembodiments of the present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of embodiments of the present invention.

These computer readable program instructions may be provided to aprocessor of a computer, or other programmable data processing apparatusto produce a machine, such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, create means for implementing the functions/acts specified inthe flowchart and/or block diagram block or blocks. These computerreadable program instructions may also be stored in a computer readablestorage medium that can direct a computer, a programmable dataprocessing apparatus, and/or other devices to function in a particularmanner, such that the computer readable storage medium havinginstructions stored therein comprises an article of manufactureincluding instructions which implement aspects of the function/actspecified in the flowchart and/or block diagram block or blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

While the disclosure outlines exemplary embodiments, it will beappreciated that variations and modifications will occur to thoseskilled in the art. For example, although the illustrative embodimentsare described herein as a series of acts or events, it will beappreciated that the present invention is not limited by the illustratedordering of such acts or events unless specifically stated. Some actsmay occur in different orders and/or concurrently with other acts orevents apart from those illustrated and/or described herein, inaccordance with the invention. In addition, not all illustrated stepsmay be required to implement a methodology in accordance withembodiments of the present invention. Furthermore, the methods accordingto embodiments of the present invention may be implemented inassociation with the formation and/or processing of structuresillustrated and described herein as well as in association with otherstructures not illustrated. Moreover, in particular regard to thevarious functions performed by the above described components(assemblies, devices, circuits, etc.), the terms used to describe suchcomponents are intended to correspond, unless otherwise indicated, toany component which performs the specified function of the describedcomponent (i.e., that is functionally equivalent), even though notstructurally equivalent to the disclosed structure which performs thefunction in the herein illustrated exemplary embodiments of theinvention. In addition, while a particular feature of embodiments of theinvention may have been disclosed with respect to only one of severalembodiments, such feature may be combined with one or more features ofthe other embodiments as may be desired and advantageous for any givenor particular application. Therefore, it is to be understood that theappended claims are intended to cover all such modifications and changesthat fall within the true spirit of embodiments of the invention.

What is claimed is:
 1. A computer-implemented method for identifyingwaste in a process, comprising: receiving an image of one or morediscarded products from a camera; performing an object count process onthe received image to identify an amount of the one or more discardedproducts within the image; acquiring metadata relating to the amount ofthe one or more identified discarded products from the received image;obtaining an amount of used product; recording the metadata; determiningan overage amount of product as a function of the acquired metadata anda factor of the amount of used product; deriving a suggestion for wastereduction based on the determination; and generating a report based onthe recorded metadata, wherein the report includes the suggestion forwaste reduction.
 2. The method of claim 1, wherein the acquiring themetadata comprises: receiving an image of a discarded product;performing an object recognition process on the received image toidentify the one or more discarded products within the image; andextracting image metadata for the received image.
 3. The method of claim2, wherein performing an object recognition process on the receivedimage comprises performing an edge detection process.
 4. The method ofclaim 3, wherein performing an object recognition process furthercomprises performing a color analysis.
 5. The method of claim 4, whereinperforming an object recognition process further comprises using one ormore image classifiers.
 6. The method of claim 1, wherein acquiringmetadata comprises acquiring metadata for a food product.
 7. The methodof claim 1, further comprising analyzing the metadata with a rulesengine.
 8. The method of claim 7, further comprising: receiving salesdata for a predetermined time duration from a point of sale system; anddetermining an amount of used product during the predetermined timeduration based on the received data; wherein the rules engine determinesan overage amount of product as a function of the acquired metadata forthe discarded product and a factor of the amount of used product.
 9. Themethod of claim 1, wherein the acquiring the metadata comprises:receiving a natural language description of a discarded product;performing a natural language analysis of the natural languagedescription to identify one or more discarded products within thenatural language description; and performing an entity detection processfor the natural language description.
 10. The method of claim 9, whereinthe natural language description comprises a speech fragment, andfurther comprising performing a speech-to-text process on the speechfragment prior to performing the natural language analysis.
 11. Themethod of claim 9, wherein performing a natural language analysisincludes performing at least one of bigram processing, disambiguation,part-of-speech analysis, and anaphora resolution.
 12. The method ofclaim 1, wherein the generating the report further includes generatingan indicator of potential waste.
 13. The method of claim 12, furthercomprising using a social media trend to derive the indicator ofpotential waste.
 14. The method of claim 12, further comprising using aweather forecast to derive the indicator of potential waste.
 15. Themethod of claim 12, further comprising using a traffic report to derivethe indicator of potential waste.
 16. The method of claim 12, furthercomprising using a sports team schedule to derive the indicator ofpotential waste.
 17. The method of claim 1, wherein the generating thereport further includes generating a substitution suggestion.
 18. Themethod of claim 1, wherein the generating the report further includesgenerating a process change suggestion.
 19. A computer systemcomprising: a processor; a memory coupled to the processor, the memorycontaining instructions, that when executed by the processor, performthe steps of: receiving an image of one or more discarded products froma camera; performing an object count process on the received image toidentify an amount of the one or more discarded products within theimage; acquiring metadata relating to the amount of the one or moreidentified discarded products from the received image; obtaining anamount of used product; recording the metadata; determining an overageamount of product as a function of the acquired metadata and a factor ofthe amount of used product; deriving a suggestion for waste reductionbased on the determination; and generating a report based on therecorded metadata, wherein the report includes the suggestion for wastereduction.
 20. A computer program product for identifying waste in aprocess, for an electronic computing device comprising a computerreadable storage medium having program instructions embodied therewith,the program instructions executable by a processor to cause theelectronic device to: receive an image of one or more discarded productsfrom a camera; perform an object count process on the received image toidentify an amount of the one or more discarded products within theimage; acquire metadata relating to the amount of the one or moreidentified discarded products from the received image; obtain an amountof used product; record the metadata; determine an overage amount ofproduct as a function of the acquired metadata and a factor of theamount of used product; derive a suggestion for waste reduction based onthe determination; and generate a report based on the recorded metadata,wherein the report includes the suggestion for waste reduction. receivean image of one or more discarded products from a camera; perform anobject count process on the received image to identify an amount of theone or more discarded products within the image; acquire metadatarelating to the amount of the one or more identified discarded productsfrom the received image; obtain an amount of used product; record themetadata; determine an overage amount of product as a function of theacquired metadata and a factor of the amount of used product; derive asuggestion for waste reduction based on the determination; and generatea report based on the recorded metadata, wherein the report includes thesuggestion for waste reduction.